Brain cognitive activity is commonly determined from the scalp distribution of signal parameters following artifact rejection and signal analysis of electroencephalograms (EEG) measured with a scalp site electrode EEG data collection system. The signal analysis may be spectral analysis or multivariate autoregressive analysis, resulting in frequency band decompositions of the site signals; the distribution depends on the reference electrode used with the data collection system. Artifacts potentials are predominately generated from ocular sources such as eye-movements or blinks, or from muscular sources such as with facial or limb movements. In the art, artifact rejection techniques are employed to isolate EEG segments with artifacts from the data set based on such characteristics as excessive signal amplitude (>+50 uv or <50 uv), trend (+/−70 uv/epoch size), spectral content (ocular <2 Hz; muscular >40 Hz), and amplitude kurtosis (>3), among others, following baseline removal. Following artifact rejection, blind source separation using independent component analysis commonly based on signal amplitude kurtosis, may be applied to the reduced data set to derive independent signal sources, which are then separated into artifacts and cortical sources by signal characteristics such as waveform, spectral content, and kurtosis, as well as location within a standard shell head as derived from the signal scalp distribution for the source; artifact sources are located outside the cortex about the skull, while cortical sources are distributed within. The result is a reduced set of cortical component sources, the potentials of which are additively projected back to the scalp sites from the source locations for further signal analysis; since the source number is less than the number of scalp sites, the back-projection is ill-conditioned and for that reason commonly approximated using the Moore-Penrose pseudo-inversion matrix with singular value decomposition, where the approximation is improved by increasing the number of scalp electrode sites.
In the art, data analysis is applied to the back-projected scalp site potentials as measures of cortical functioning, where signal analysis may be non-parametric spectral analysis or parametric multivariate autoregressive analysis, resulting in frequency band decompositions of the site signals, commonly in the delta (2-5 Hz), theta (5-7 Hz), alpha (I: 8-10 Hz; II: 10-12 Hz), and the beta (12-20 Hz) frequency band ranges. In some prior art, data analysis is applied directly to the cortical component sources resulting from blind source separation without back projection to the sites. Application of the autoregressive process results in noise covariance and autoregressive coefficients, from which spectrums may be computed for the sites and the interactions among the sites. These methods can be extended using short-term Fourier analysis or wavelet analysis to produce a time-frequency spectral data analysis for the sites. The spectral results may be decomposed into spectrum band power; spectrum coherence computed from the power spectral matrix; and Granger causality for the coherence between the sites as a network. Further refinement in decompositions may be found in the prior art where the spectrum coherence (as a measure of mutual synchronicity among sites), may be decomposed into different measures of the Granger causality for the direction of information flow among sites. These measures include the directed coherence (DC), which is the ratio of the transfer function between two nodes, and the square root of the auto power of one of the nodes; and the directed transfer function (DTF), the ratio of the transfer function between two nodes, and the square root of the dot product of the vector of the transfer functions for the inputs to one of the nodes with its conjugate transpose, as well as the partial directed coherence, a function of the autoregressive model coefficients used in the spectral analysis. In further developments in the prior art, graph theory measures are applied for analysis of the sites as nodes of a network, by using small world network metrics computed from the cross-correlation matrices for the sites, such as node degree (average number of connections nodes), clustering coefficient (ratio of existing connections to all possible), diameter (shortest path between nodes), and efficiency (measure of number of parallel connections among nodes), among others. In experimental studies, statistical analysis may be applied to these measures by treatments for study results.
Commonly in the art, the scalp topological and power spectrum frequency distributions for site signals are reported in the literature as indicators of cognitive processing. Some examples of research results found in the prior art, follow: Alpha band (8-12 Hz) power has been shown to decrease with task performance, at least for arithmetic, recalling, and visual and auditory memory tasks. Theta band (4-7 Hz) power increased during spatial and verbal tasks, with a large increase over the right hemisphere in the spatial task. Frontal theta activity is associated with increased mental processing during challenging tasks; prefrontal excitation and lateralization in the anterior regions are indicative of high mental workload. Theta coherence between prefrontal and posterior cortical regions occurs with cognitive switching between tasks during the task setup and associated memory transfer, followed by upper alpha band suppression with memory processing at completion of task setup. A repetitive task sequence is associated with suppressed lower alpha band involved in attention and expectancy. Similarly, reported in the literature are results for cortical component sources resulting from blind source separation; in particular, results for driving studies in which such cortical sources show tradeoffs in activations between the frontal cortex and the motor cortex depending upon whether activities are a response to course deviation or problem solving during simulated driving.
While these are important results, as will be demonstrated in the specifications our own research using the mechanics of this invention for the evaluation of the effectiveness of these EEG analysis techniques of the prior art has shown limits to the validity at three levels: isolating the true cortical signal from the data for analysis, specifying the location of the sources within the cortex for cortical networks, and the manner of representing source activation for network analysis. Application of the invention mechanics to the prior art techniques of artifact rejection and blind source separation for isolation of the true cortical signal has shown that the resulting sources are not truly separated, since the cortical component sources retain some artifact and not all cortical signal is removed from the artifact sources. The retention depends in part upon the artifact rejection thresholds and the ratio of artifact-noise to the pure cortical signal in the data, since segments highly contaminated will be rejected leaving a reduced data set with mostly signal, while those with moderate artifacts may fail rejection and be included with the pure signal. The signal amplitude for the cortical based signal is on the order of micro-volts, while that of the artifacts is on the order of millivolts, although the order may be much less during micro-saccade eye-movements or fine muscle movements and initiation; further, muscular artifacts can have spectrum components within the 25 Hz range of the pure signal. Another problem is that the data set with artifacts may correspond to the activity period of interest while those without may correspond to periods between activities, and analysis of this data may result in a resting state measure of cortical activity. While, the blind source separation process separates out eye-movements and muscle sources from cortical sources by source localization, the process does not fully separate the spectrum contents since as has been noted, both muscular and ocular artifacts have spectrum components within the pure signal range. Again, the accuracy of the back-projection method, an ill-conditioned process, depends upon the number of scalp sites, and the accuracy deteriorates rapidly for smaller site numbers such as on the order of 19-sites for the standard 10-20 electrode system.
Therefore, if measurement of electroencephalograms (EEG) with a scalp site electrode EEG data collection system is to be useful in real life applications such as in moving vehicles with operator control, there is a need in the art for a method and apparatus for more accurately determining cerebral source excitations from the EEG measurements.